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whisper-small-multilingual-spoken-ner-pipeline-step-1

Transformers
Safetensors
whisper_for_slu
whisper-event
Generated from Trainer
Eval Results
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whisper-small-multilingual-spoken-ner-pipeline-step-1 / README.md
Quentin Meeus
Train NER module for 5000 steps
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metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - facebook/voxpopuli
metrics:
  - wer
model-index:
  - name: WhisperForSpokenNER
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: facebook/voxpopuli de+es+fr+nl
          type: facebook/voxpopuli
          config: de+es+fr+nl
          split: None
        metrics:
          - name: Wer
            type: wer
            value: 0.11695951699047914

WhisperForSpokenNER

This model is a fine-tuned version of openai/whisper-small on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:

  • Loss: 75.5138
  • F1 Score: 0.6260
  • Label F1: 0.8282
  • Wer: 0.1170

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Score Label F1 Wer
294.2045 0.09 200 219.4521 0.3694 0.6423 0.1170
172.5491 0.18 400 158.4206 0.5112 0.7076 0.1170
152.1994 0.27 600 148.5779 0.5501 0.7391 0.1170
140.706 0.36 800 151.8108 0.5413 0.7324 0.1170
125.5897 0.45 1000 138.0534 0.5601 0.7432 0.1170
122.0436 0.54 1200 118.2416 0.5636 0.7724 0.1170
117.7194 0.63 1400 116.8705 0.5910 0.7772 0.1170
119.8977 0.71 1600 106.7047 0.5905 0.7833 0.1170
105.5846 0.8 1800 105.5354 0.5756 0.7774 0.1170
106.7833 0.89 2000 101.9971 0.5875 0.7922 0.1170
101.8875 0.98 2200 98.1714 0.5945 0.8016 0.1170
87.7438 1.07 2400 97.7943 0.6040 0.7967 0.1170
86.1916 1.16 2600 93.9310 0.6033 0.7964 0.1170
85.3271 1.25 2800 92.3677 0.6188 0.8146 0.1170
83.1457 1.34 3000 89.3458 0.6028 0.8116 0.1170
79.4126 1.43 3200 86.8935 0.6061 0.8094 0.1170
74.7596 1.52 3400 82.3525 0.6147 0.8224 0.1170
79.5526 1.61 3600 80.6440 0.6116 0.8153 0.1170
76.0212 1.7 3800 80.1555 0.6150 0.8216 0.1170
70.2905 1.79 4000 80.9369 0.6152 0.8177 0.1170
68.0936 1.88 4200 77.4738 0.6181 0.8206 0.1170
72.6116 1.97 4400 75.5524 0.6236 0.8276 0.1170
61.0175 2.06 4600 75.7015 0.6242 0.8249 0.1170
60.3508 2.14 4800 75.5521 0.6253 0.8270 0.1170
57.4103 2.23 5000 75.5138 0.6260 0.8282 0.1170

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1